7 papers
FD-RAG: Federated Dual-System Retrieval-Augmented Generation
Tianhao Gao, Kai Yang, Yiyang Li
Retrieval-augmented generation (RAG) has emerged as a paradigm for grounding large language models in external knowledge, yet most existing RAG systems assume centralized knowledge…
Resting Neurons, Active Insights: Robustifying Activation Sparsity in LLMs via Spontaneity
Haotian Xu, Jiannan Yang, Tian Gao +2
Activation sparsity offers a compelling route to accelerate large language model (LLM) inference by selectively suppressing hidden activations, yet existing approaches exhibit seve…
Disentangled Representation Learning for Parametric Partial Differential Equations
Ning Liu, Lu Zhang, Tian Gao +1
Neural operators (NOs) excel at learning mappings between function spaces, serving as efficient forward solution approximators for PDE-governed systems. However, as black-box solve…
Stable Preference Optimization: A Bilevel Approach to Catastrophic Preference Shift
Chengtao Jian, Kai Yang, Tianhao Gao +5
Direct Preference Learning has emerged as a dominant offline paradigm for preference optimization. Most of these methods are based on the Bradley-Terry (BT) model for pairwise pref…
FANoise: Singular Value-Adaptive Noise Modulation for Robust Multimodal Representation Learning
Jiaoyang Li, Jun Fang, Tianhao Gao +5
Representation learning is fundamental to modern machine learning, powering applications such as text retrieval and multimodal understanding. However, learning robust and generaliz…
Learning Causal Graphs at Scale: A Foundation Model Approach
Naiyu Yin, Tian Gao, Yue Yu
Due to its human-interpretability and invariance properties, Directed Acyclic Graph (DAG) has been a foundational tool across various areas of AI research, leading to significant a…